Improving Prediction Accuracy of a Rate-Based Model of an MEA-BasedCarbon Capture Process for Large-Scale Commercial Deployment

摘要

Carbon capture and storage （CCS） technology will play a critical role in reducing anthropogenic carbondioxide （CO2） emission from fossil-fired power plants and other energy-intensive processes. However, theincrement of energy cost caused by equipping a carbon capture process is the main barrier to its commer-cial deployment. To reduce the capital and operating costs of carbon capture, great efforts have been madeto achieve optimal design and operation through process modeling, simulation, and optimization. Accuratemodels form an essential foundation for this purpose. This paper presents a study on developing a moreaccurate rate-based model in Aspen Plus for the monoethanolamine （MEA）-based carbon capture processby multistage model validations. The modeling framework for this process was established first. The steady-state process model was then developed and validated at three stages, which included a thermodynamicmodel, physical properties calculations, and a process model at the pilot plant scale, covering a wide rangeof pressures, temperatures, and CO2 loadings. The calculation correlations of liquid density and interfacialarea were updated by coding Fortran subroutines in Aspen Plus. The validation results show that the cor-relation combination for the thermodynamic model used in this study has higher accuracy than those ofthree other key publications and the model prediction of the process model has a good agreement with thepilot plant experimental data. A case study was carried out for carbon capture from a 250 MWe combinedcycle gas turbine （CCGT） power plant. Shorter packing height and lower specific duty were achieved usingthis accurate model.